AI-driven investing is a term that encompasses various forms of automated and intelligent investment solutions that leverage artificial intelligence (AI) and data analytics to optimise portfolio performance, reduce costs, and enhance the customer experience. In recent years, the financial industry has witnessed a remarkable transformation with the advent of AI and its integration into investment strategies. This has led to the rise of robo-advisors and algorithmic trading, which have revolutionised the way individuals and institutions invest.
Robo-advisors
Robo-advisors use mathematical algorithms to assess the risk profile, financial goals and preferences of the clients and then allocate their assets across different asset classes, such as stocks, bonds, ETFs, etc. Robo-advisors also monitor and rebalance the portfolios periodically to maintain the optimal risk-return trade-off.
Robo-advisors offer several benefits to investors, such as:
Lower fees: Robo-advisors typically charge a fraction of the fees that traditional human advisors charge, ranging from 0.25% to 1% of the assets under management (AUM) per year. This can save investors thousands of dollars over time.
Higher accessibility: Robo-advisors are available 24/7 through online or mobile platforms and often have lower minimum investment requirements than human advisors, making them more accessible to a wider range of investors.
Greater transparency: Robo-advisors provide clear and detailed information about their investment strategies, methodologies, assumptions, and performance, allowing investors to understand how their money is being invested and managed.
Reduced bias: Robo-advisors eliminate the potential for human errors, emotions, and conflicts of interest that may affect the quality of investment advice and decisions.
The growth of robo-advisors
The popularity of robo-advisors has soared in recent years. Robo-advisors are expected to grow at a compound annual growth rate (CAGR) of 25.6% from 2020 to 2027, reaching $987.4 billion by 2027. This substantial growth can be attributed to several factors, including low fees, ease of use, and the ability to access professional investment services without the need for a human advisor. USA is the largest market for robo-advisors, accounting for 75% of the global AUM in 2019, followed by Europe (14%) and Asia-Pacific (6%).
Algorithmic trading
Algorithmic trading is a method of executing orders in the financial markets using pre-programmed rules and strategies based on various factors, such as price, volume, time, or market conditions. Algorithmic trading can be used for various purposes, such as market making, arbitrage, trend following, or high-frequency trading. However, algorithmic trading is not limited to these conventional applications. From this perspective, we will explore some of the new trends and challenges in algorithmic trading that are emerging in the current market environment. Some of the new trends in algorithmic trading are:
Algorithmic trading of alternative assets: Algorithmic trading is not only applied to traditional assets, such as stocks or bonds, but also to alternative assets, such as cryptocurrencies, commodities, or derivatives. These assets have different characteristics and dynamics than conventional assets, such as higher volatility, lower liquidity, or complex pricing mechanisms. Therefore, algorithmic trading of alternative assets requires more sophisticated models, data sources, and execution platforms to capture the opportunities and risks in these markets.
Algorithmic trading with social media data: Social media platforms, such as Twitter or Reddit, have become influential sources of information and sentiment for investors and traders. Social media data can provide insights into market trends, events, opinions, or emotions that affect the prices of securities. Therefore, algorithmic trading with social media data involves using natural language processing (NLP), text mining, or machine learning techniques to extract relevant signals from social media posts or comments and use them to generate or execute trading strategies.
Algorithmic trading with reinforcement learning: Reinforcement learning is a branch of machine learning that enables an agent to learn from its own actions and feedback in an uncertain environment. Reinforcement learning can be applied to algorithmic trading to create self-learning and adaptive trading systems that can optimise their performance over time. Reinforcement learning can help algorithmic trading systems cope with changing market conditions or non-stationary data distributions.
Algorithmic trading is becoming more prevalent and dominant in the financial markets as technology advances and competition intensifies.
Surge in algorithmic trading
Algorithmic trading has experienced remarkable growth, driven by advancements in computing power and the availability of vast amounts of financial data. According to a study by Grand View Research, the global algorithmic trading market is expected to reach $25.7 billion by 2025, growing at a CAGR of 11.1% from 2019 to 2025. USA is the largest market for algorithmic trading, accounting for 52% of the global market share in 2018, followed by Europe (32%) and Asia-Pacific (12%).
Conclusion
AI-driven investing, powered by robo-advisors and algorithmic trading, has revolutionised the investment landscape. These technologies’ growth and adoption have reshaped how individuals and institutions invest, offering cost-effective and efficient solutions. While challenges and ethical considerations remain, the continued advancement of AI in finance holds significant potential for enhancing investment outcomes and market efficiency in the years to come.
(The writer is the founder and chief executive officer of financial services company Alice Blue)